Advances in the three-dimensional imaging and subsequent computational representation of lithium-ion battery electrodes have made it possible to perform detailed simulations on reconstructed electrode mesostructures. These particle-scale simulation capabilities can be utilized to analyze the effect of the manufacturing process and can also inform macroscale (cell- or module-level) battery models, most notably through effective transport properties. For these simulations to be realistic and accurate, quality computational meshes and sufficient domain sizes (1000+ active material particles) must be achieved.

Additionally, imaging techniques, such as x-ray tomography, that can provide sufficiently large domain sizes are often unable to clearly differentiate between the non-active electrode phases (binder, conductive additive, and void/electrolyte). This forces models to make assumptions about the morphology of non-active phases or neglect their contributions altogether. Our recent work has focused on addressing these challenges, and we have developed a novel “binder bridge” placement algorithm to introduce a composite binder morphology to a large many-particle domain that mimics morphologies seen in high-resolution imaging where nonactive phases can be differentiated.

We use this approach to study the effect of the electrode manufacturing process on effective electrode properties by simulating electrodes manufactured using various calendaring pressures and slurry compositions.